ZipRecruiter

Log In

1

Machine Learning System Engineer Jobs in Toronto, ON

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... systems meeting latency and throughput requirements Work with large-scale pathology datasets to ...

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... systems meeting latency and throughput requirements Work with large-scale pathology datasets to ...

Boson AI

Machine Learning Engineer - Enterprise

Toronto, ON · On-site

CA$150K - CA$400K/yr

We are seeking a skilled, detail-oriented, and passionate Machine Learning Engineer to join our ... Develop and deploy modern search systems (e.g., RAG, DeepSearch) to enhance model performance ...

previous page

Showing results 41-60

All JobsMachine Learning System Engineer JobsMachine Learning System Engineer Jobs in Toronto, ON

Machine Learning System Engineer information

What is a machine learning system engineer?

Machine learning system engineers are professionals who design, build, and maintain the infrastructure and systems that support machine learning models in production environments. They work at the intersection of software engineering and data science, ensuring that machine learning algorithms run efficiently, scale appropriately, and integrate seamlessly with existing applications. Their responsibilities often include data pipeline development, model deployment, monitoring, and optimization to ensure reliable and robust AI solutions.

What are the key skills and qualifications needed to thrive as a machine learning system engineer?

To thrive as a Machine Learning System Engineer, you need strong skills in computer science, statistics, machine learning algorithms, and a degree in a related field such as computer science or engineering. Proficiency with programming languages like Python or Java, experience with ML frameworks (e.g., TensorFlow, PyTorch), and knowledge of cloud platforms are typically required. Exceptional problem-solving abilities, teamwork, and effective communication are vital soft skills that help in designing scalable solutions and collaborating across teams. These skills ensure the successful development, deployment, and maintenance of reliable machine learning systems in real-world environments.

What are some common challenges machine learning system engineers face when deploying models to production environments?

Machine Learning System Engineers often encounter challenges such as ensuring model scalability, maintaining low latency, and addressing data drift once models are deployed in production. They must also work closely with software engineers, data scientists, and DevOps teams to integrate models seamlessly into existing systems and monitor their ongoing performance. Additionally, balancing computational resources and optimizing for cost efficiency while ensuring high reliability can be complex, making collaboration and clear communication essential in this role.

What is the difference between Machine Learning System Engineer vs Data Scientist?

AspectMachine Learning System EngineerData Scientist
CredentialsBachelor's or Master's in CS, ML, or related fields; certifications in ML or cloud platformsBachelor's or Master's in Statistics, Data Science, or related fields; certifications in data analysis or ML
Work EnvironmentDevelops, deploys, and maintains ML systems; collaborates with engineering teamsAnalyzes data, builds models, interprets results; works closely with business teams
Industry UsageTech companies, AI startups, enterprises deploying ML systemsResearch institutions, analytics firms, tech companies

While both roles involve machine learning, Machine Learning System Engineers focus on building and maintaining scalable ML systems, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in technical focus and responsibilities.

Infographic showing various Machine Learning System Engineer job openings in Toronto, ON as of September 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

Sr. Machine Learning Software Verification Engineer

Toronto, ON • On-site

Talentlab
See more jobs at Talentlabexternal

Full-time

Posted 27 days ago

Job description

AI Software Test / Validation Engineer

Location: Toronto, ON
Job Type: Full-Time
Industry: Technology / AI / Semiconductor

About the Role

Our client is a global technology leader developing next-generation AI and machine learning solutions for on-device applications across mobile, automotive, IoT, and computing platforms.

We are looking for an AI Software Test / Validation Engineer to join a team focused on validating high-performance AI software and machine learning solutions. This role will involve developing test strategies, automation frameworks, and test cases to ensure the quality, reliability, and performance of AI software running on next-generation computing platforms.

This is a software testing and validation role, not a data science position.

What You’ll Do
  • Validate AI and machine learning software SDK features on computing platforms.
  • Develop and execute test cases to verify AI software functionality, performance, and reliability.
  • Test applications and developer tools supporting the AI software stack.
  • Develop, maintain, and improve automated software test frameworks.
  • Analyze customer use cases and translate requirements into effective test scenarios.
  • Work closely with software development teams to identify, reproduce, and resolve issues.
  • Support regression testing, triage failures, analyze defects, and track issues through resolution.
  • Contribute to continuous integration and automated testing processes.
  • Help improve overall software quality, test coverage, and validation processes.
What You’ll Bring
  • 2+ years of professional experience in software development, software testing, or validation.
  • Bachelor's degree in Computer Science, Engineering, or a related discipline; Master's or PhD is an asset.
  • 1+ year of experience with Python and scripting languages.
  • 1+ year of experience with C++.
  • Experience with Jenkins or other CI/CD tools.
  • Strong software debugging and problem-solving skills.
  • Experience with Git or other version control systems.
  • Experience working in Linux and Windows development environments.
  • Strong communication skills and the ability to collaborate effectively within a multidisciplinary engineering team.
Nice to Have
  • Experience with embedded systems development or testing.
  • Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or ONNX.
  • Knowledge of modern Generative AI / GenAI models.
  • Experience developing automated test frameworks for software or embedded platforms.
  • Familiarity with AI/ML software stacks and hardware-accelerated computing.
Why This Role?

This is an opportunity to work at the intersection of AI, software engineering, and automated testing, helping bring next-generation on-device AI technologies from development into production. You’ll work alongside experienced software and AI engineers while contributing directly to the quality and reliability of cutting-edge AI platforms.